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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code>.make_classification</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-make-classification">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.make_classification</span></code></a></li>
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  <div class="section" id="sklearn-datasets-make-classification">
<h1><a class="reference internal" href="../classes.html#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code></a>.make_classification<a class="headerlink" href="#sklearn-datasets-make-classification" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.datasets.make_classification">
<code class="sig-prename descclassname">sklearn.datasets.</code><code class="sig-name descname">make_classification</code><span class="sig-paren">(</span><em class="sig-param">n_samples=100</em>, <em class="sig-param">n_features=20</em>, <em class="sig-param">n_informative=2</em>, <em class="sig-param">n_redundant=2</em>, <em class="sig-param">n_repeated=0</em>, <em class="sig-param">n_classes=2</em>, <em class="sig-param">n_clusters_per_class=2</em>, <em class="sig-param">weights=None</em>, <em class="sig-param">flip_y=0.01</em>, <em class="sig-param">class_sep=1.0</em>, <em class="sig-param">hypercube=True</em>, <em class="sig-param">shift=0.0</em>, <em class="sig-param">scale=1.0</em>, <em class="sig-param">shuffle=True</em>, <em class="sig-param">random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/datasets/_samples_generator.py#L36"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.make_classification" title="Permalink to this definition">¶</a></dt>
<dd><p>Generate a random n-class classification problem.</p>
<p>This initially creates clusters of points normally distributed (std=1)
about vertices of an <code class="docutils literal notranslate"><span class="pre">n_informative</span></code>-dimensional hypercube with sides of
length <code class="docutils literal notranslate"><span class="pre">2*class_sep</span></code> and assigns an equal number of clusters to each
class. It introduces interdependence between these features and adds
various types of further noise to the data.</p>
<p>Without shuffling, <code class="docutils literal notranslate"><span class="pre">X</span></code> horizontally stacks features in the following
order: the primary <code class="docutils literal notranslate"><span class="pre">n_informative</span></code> features, followed by <code class="docutils literal notranslate"><span class="pre">n_redundant</span></code>
linear combinations of the informative features, followed by <code class="docutils literal notranslate"><span class="pre">n_repeated</span></code>
duplicates, drawn randomly with replacement from the informative and
redundant features. The remaining features are filled with random noise.
Thus, without shuffling, all useful features are contained in the columns
<code class="docutils literal notranslate"><span class="pre">X[:,</span> <span class="pre">:n_informative</span> <span class="pre">+</span> <span class="pre">n_redundant</span> <span class="pre">+</span> <span class="pre">n_repeated]</span></code>.</p>
<p>Read more in the <a class="reference internal" href="../../datasets/index.html#sample-generators"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>n_samples</strong><span class="classifier">int, optional (default=100)</span></dt><dd><p>The number of samples.</p>
</dd>
<dt><strong>n_features</strong><span class="classifier">int, optional (default=20)</span></dt><dd><p>The total number of features. These comprise <code class="docutils literal notranslate"><span class="pre">n_informative</span></code>
informative features, <code class="docutils literal notranslate"><span class="pre">n_redundant</span></code> redundant features,
<code class="docutils literal notranslate"><span class="pre">n_repeated</span></code> duplicated features and
<code class="docutils literal notranslate"><span class="pre">n_features-n_informative-n_redundant-n_repeated</span></code> useless features
drawn at random.</p>
</dd>
<dt><strong>n_informative</strong><span class="classifier">int, optional (default=2)</span></dt><dd><p>The number of informative features. Each class is composed of a number
of gaussian clusters each located around the vertices of a hypercube
in a subspace of dimension <code class="docutils literal notranslate"><span class="pre">n_informative</span></code>. For each cluster,
informative features are drawn independently from  N(0, 1) and then
randomly linearly combined within each cluster in order to add
covariance. The clusters are then placed on the vertices of the
hypercube.</p>
</dd>
<dt><strong>n_redundant</strong><span class="classifier">int, optional (default=2)</span></dt><dd><p>The number of redundant features. These features are generated as
random linear combinations of the informative features.</p>
</dd>
<dt><strong>n_repeated</strong><span class="classifier">int, optional (default=0)</span></dt><dd><p>The number of duplicated features, drawn randomly from the informative
and the redundant features.</p>
</dd>
<dt><strong>n_classes</strong><span class="classifier">int, optional (default=2)</span></dt><dd><p>The number of classes (or labels) of the classification problem.</p>
</dd>
<dt><strong>n_clusters_per_class</strong><span class="classifier">int, optional (default=2)</span></dt><dd><p>The number of clusters per class.</p>
</dd>
<dt><strong>weights</strong><span class="classifier">array-like of shape (n_classes,) or (n_classes - 1,),              (default=None)</span></dt><dd><p>The proportions of samples assigned to each class. If None, then
classes are balanced. Note that if <code class="docutils literal notranslate"><span class="pre">len(weights)</span> <span class="pre">==</span> <span class="pre">n_classes</span> <span class="pre">-</span> <span class="pre">1</span></code>,
then the last class weight is automatically inferred.
More than <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> samples may be returned if the sum of
<code class="docutils literal notranslate"><span class="pre">weights</span></code> exceeds 1.</p>
</dd>
<dt><strong>flip_y</strong><span class="classifier">float, optional (default=0.01)</span></dt><dd><p>The fraction of samples whose class is assigned randomly. Larger
values introduce noise in the labels and make the classification
task harder.</p>
</dd>
<dt><strong>class_sep</strong><span class="classifier">float, optional (default=1.0)</span></dt><dd><p>The factor multiplying the hypercube size.  Larger values spread
out the clusters/classes and make the classification task easier.</p>
</dd>
<dt><strong>hypercube</strong><span class="classifier">boolean, optional (default=True)</span></dt><dd><p>If True, the clusters are put on the vertices of a hypercube. If
False, the clusters are put on the vertices of a random polytope.</p>
</dd>
<dt><strong>shift</strong><span class="classifier">float, array of shape [n_features] or None, optional (default=0.0)</span></dt><dd><p>Shift features by the specified value. If None, then features
are shifted by a random value drawn in [-class_sep, class_sep].</p>
</dd>
<dt><strong>scale</strong><span class="classifier">float, array of shape [n_features] or None, optional (default=1.0)</span></dt><dd><p>Multiply features by the specified value. If None, then features
are scaled by a random value drawn in [1, 100]. Note that scaling
happens after shifting.</p>
</dd>
<dt><strong>shuffle</strong><span class="classifier">boolean, optional (default=True)</span></dt><dd><p>Shuffle the samples and the features.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None (default)</span></dt><dd><p>Determines random number generation for dataset creation. Pass an int
for reproducible output across multiple function calls.
See <a class="reference internal" href="../../glossary.html#term-random-state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array of shape [n_samples, n_features]</span></dt><dd><p>The generated samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array of shape [n_samples]</span></dt><dd><p>The integer labels for class membership of each sample.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_blobs</span></code></a></dt><dd><p>simplified variant</p>
</dd>
<dt><a class="reference internal" href="sklearn.datasets.make_multilabel_classification.html#sklearn.datasets.make_multilabel_classification" title="sklearn.datasets.make_multilabel_classification"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_multilabel_classification</span></code></a></dt><dd><p>unrelated generator for multilabel tasks</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>The algorithm is adapted from Guyon [1] and was designed to generate
the “Madelon” dataset.</p>
<p class="rubric">References</p>
<dl class="citation">
<dt class="label" id="r36a464cc3878-1"><span class="brackets">1</span></dt>
<dd><p>I. Guyon, “Design of experiments for the NIPS 2003 variable
selection benchmark”, 2003.</p>
</dd>
</dl>
</dd></dl>

<div class="section" id="examples-using-sklearn-datasets-make-classification">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.make_classification</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-make-classification" title="Permalink to this headline">¶</a></h2>
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<img alt="../../_images/sphx_glr_plot_discretization_classification_thumb.png" src="../../_images/sphx_glr_plot_discretization_classification_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/preprocessing/plot_discretization_classification.html#sphx-glr-auto-examples-preprocessing-plot-discretization-classification-py"><span class="std std-ref">Feature discretization</span></a></span><a class="headerlink" href="#id13" title="Permalink to this image">¶</a></p>
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</div><div class="sphx-glr-thumbcontainer" tooltip="The following example illustrates the effect of scaling the regularization parameter when using..."><div class="figure align-default" id="id15">
<img alt="../../_images/sphx_glr_plot_svm_scale_c_thumb.png" src="../../_images/sphx_glr_plot_svm_scale_c_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/svm/plot_svm_scale_c.html#sphx-glr-auto-examples-svm-plot-svm-scale-c-py"><span class="std std-ref">Scaling the regularization parameter for SVCs</span></a></span><a class="headerlink" href="#id15" title="Permalink to this image">¶</a></p>
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